Scikit-learn offers a function normalize() that lets you apply various normalizations. The "make it sum to 1" is called L1-norm. Therefore:
from sklearn.preprocessing import normalize
matrix = numpy.arange(0,27,3).reshape(3,3).astype(numpy.float64)
# array([[ 0., 3., 6.],
# [ 9., 12., 15.],
# [ 18., 21., 24.]])
normed_matrix = normalize(matrix, axis=1, norm='l1')
# [[ 0. 0.33333333 0.66666667]
# [ 0.25 0.33333333 0.41666667]
# [ 0.28571429 0.33333333 0.38095238]]
Now your rows will sum to 1.